{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   admit       gpa         gre\n",
      "0      0  3.177277  594.102992\n",
      "1      0  3.412655  631.528607\n",
      "2      0  2.728097  553.714399\n",
      "3      0  3.093559  551.089985\n",
      "4      0  3.141923  537.184894\n"
     ]
    },
    {
     "data": {
      "image/png": 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LgH/KzPtqHqskSSqg1jUXmfmR6ndavAMYBO4AzszM+6sqq4Hj2urfGBHLgTfRXGvxY5pX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\ny/y0pbt5t80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x81e8d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "admissions = pd.read_csv(\"admissions.csv\")\n",
    "print admissions.head()\n",
    "plt.scatter(admissions['gpa'], admissions['admit'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XnUZSrbBFQ1KH3HgjjBkDDzwAbpgsqaNs0ZA0T9On55km224LG25YdBpJtcQW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\nXytY8SySywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6b30830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Logit Function\n",
    "def logit(x):\n",
    "    # np.exp(x) raises x to the exponential power, ie e^x. e ~= 2.71828\n",
    "    return np.exp(x)  / (1 + np.exp(x)) \n",
    "    \n",
    "# Generate 50 real values, evenly spaced, between -6 and 6.\n",
    "x = np.linspace(-6,6,50, dtype=float)\n",
    "\n",
    "# Transform each number in t using the logit function.\n",
    "y = logit(x)\n",
    "\n",
    "# Plot the resulting data.\n",
    "plt.plot(x, y)\n",
    "plt.ylabel(\"Probability\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_model = LinearRegression()\n",
    "linear_model.fit(admissions[[\"gpa\"]], admissions[\"admit\"])\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(admissions[[\"gpa\"]], admissions[\"admit\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oiEiT1LMg0oYFQcDUqVN5+uln8T/OdYccVgIBvjahrt3Edopctuw1hQQROSKF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\nv7sL3TaglO8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x836e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(admissions[[\"gpa\"]], admissions[\"admit\"])\n",
    "pred_probs = logistic_model.predict_proba(admissions[[\"gpa\"]])\n",
    "plt.scatter(admissions[\"gpa\"], pred_probs[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VqpZwARARX6J19eJ3gAXAnwN3ZOZ/aKtzH/C+zPx8FSz+J63bUc9g6zUbj2Xm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\n/7jXoMbF7hyaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x80d1df0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(admissions[[\"gpa\"]], admissions[\"admit\"])\n",
    "fitted_labels = logistic_model.predict(admissions[[\"gpa\"]])\n",
    "plt.scatter(admissions[\"gpa\"], fitted_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
